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Abstract We are merging a large participatory science effort with machine learning to enhance the Hobby–Eberly Telescope Dark Energy Experiment (HETDEX). Our overall goal is to remove false positives, allowing us to use lower signal-to-noise data and sources with low goodness-of-fit. With six million classifications through Dark Energy Explorers, we can confidently determine if a source is not real at over 94% confidence level when classified by at least 10 individuals; this confidence level increases for higher signal-to-noise sources. To date, we have only been able to apply this direct analysis to 190,000 sources. The full sample of HETDEX will contain around 2–3 million sources, including nearby galaxies ([Oii] emitters), distant galaxies (Lyαemitters or LAEs), false positives, and contamination from instrument issues. We can accommodate this tenfold increase by using machine learning with visually vetted samples from Dark Energy Explorers. We have already increased by over tenfold the number of sources that have been visually vetted from our previous pilot study where we only had 14,000 visually vetted LAE candidates. This paper expands on the previous work by increasing the visually vetted sample from 14,000 to 190,000. In addition, using our currently visually vetted sample, we generate a real or false positive classification for the full candidate sample of 1.2 million LAEs. We currently have approximately 17,000 volunteers from 159 countries around the world. Thus, we are applying participatory or citizen scientist analysis to our full HETDEX data set, creating a free educational opportunity that requires no prior technical knowledge. 

Abstract Investigating the impact of galaxy properties on emergent Lyαemission is crucial for reionization studies, given the sensitivity of Lyαto neutral hydrogen. This study presents an analysis of the physical characteristics of 155 star-forming galaxies, 29 with Lyαdetected, and 126 with Lyαnot detected with LyαEW < 20 Å, atz= 1.9–3.5, drawn from the MOSFIRE Deep Evolution Field survey, that have overlapping observations from the Hobby–Eberly Telescope Dark Energy Experiment survey. To unravel the interstellar medium (ISM) conditions in our sample, we developed a custom nebular line modeling algorithm based on the MAPPINGS V photoionization model grid and theemceeframework. Combining nebular-based ISM properties with photometry-based global properties, constrained viaBagpipes, we explore distinctions in the stellar and gas properties between Lyα-detected and Lyα-nondetected galaxies. Our analysis reveals statistically significant differences between the two samples in terms of stellar mass and dust attenuation (A_V) at >2σsignificance, as determined via a Kolmogorov–Smirnov test. Moreover, there are weaker (≲1σsignificance) indications that the ionization parameter and metallicity differ between the two samples. Our results demonstrate that the escape fraction of Lyα( $f_{\mathrm{esc}}^{\mathrm{Ly}\alpha }$) is inversely correlated with stellar mass, star formation rate, and dust attenuation, while it is positively correlated with the ionization parameter, with significance levels exceeding 2σ. Our findings suggest that the interstellar environments of Lyα-detected galaxies, characterized by low mass, low dust, low gas-phase metallicity, and high ionization parameters, play a pivotal role in promoting the escape of Lyαradiation.